A novel technology-explicit framework for predicting the efficiency of industrial device retrofits in stock turnover models with a case study of the pulp and paper sector

Abstract

When replacing equipment at its end of life, industries often have the option to select higher efficiency technologies. The choice to invest in an improved piece of equipment rather than a simple in-kind replacement is driven by many factors, including costs, performance, and familiarity with new technology options. Engineering models that account for equipment stock turnover, such as ENERGY 2020, typically assume that this decision is primarily driven by the difference in marginal costs: higher up front capital costs must be, at minimum, balanced by lower lifetime energy costs for an upgrade to be pursued. Stock turnover analysis requires detailed data inputs regarding the costs and performance of new equipment. For simplicity, a common approach is to develop assumed correlations that reflect the trade-off between marginal capital cost and efficiency and compare these to energy prices to select an efficiency level for new equipment. In this study, we present a novel method to develop such trade-off curves based on a technology-explicit approach rather than a qualitative correlation assumption. We generate trade-off curves for two common types of industrial devices: electric machine drive and natural gas steam generation for the case study of the Canadian pulp and paper sector. The curves demonstrate that the efficiency of new devices can be expected to vary significantly based on energy prices. At current energy prices, we find that newly purchased machine drive and steam generation devices would have an optimal efficiency level of 91% and 75%, respectively.